Unsupervised and Weakly Supervised Domain Adaptation of MRI Skull-Stripping Models Trained on Adult Data to Newborns

Omidi, Abbas

Unsupervised and Weakly Supervised Domain Adaptation of MRI Skull-Stripping Models Trained on Adult Data to Newborns

dc.contributor.advisor	Souza, Roberto
dc.contributor.author	Omidi, Abbas
dc.contributor.committeemember	Abou-Zeid, Hatem
dc.contributor.committeemember	Ovens, Katie
dc.date	2025-02
dc.date.accessioned	2025-01-15T22:18:24Z
dc.date.available	2025-01-15T22:18:24Z
dc.date.issued	2025-01-13
dc.description.abstract	The process of removing non-brain tissue signals from brain magnetic resonance imaging (MRI) is known as skull-stripping. It is a crucial preprocessing step in neuroimaging analysis, particularly for subsequent brain tissue segmentation and studying neurological disorders. Despite significant progress in deep learning-based methods for skull-stripping, data distribution shifts between adult and newborn MRI data present a major challenge, limiting the generalization of models trained on adult data when applied to newborns. This work proposes both unsupervised and weakly supervised domain adaptation techniques that leverage weakly annotated data, synthetic data, and the learning of domain-invariant features to address this challenge. First, I introduce an unsupervised method utilizing adversarial domain adaptation to align feature representations between adult and newborn MRI data, and a new contrast inversion data augmentation step to reduce the domain shift. Then, I extend this method by leveraging Gaussian Mixture Model (GMM)-generated synthetic data to enhance segmentation performance. Finally, I propose to incorporate weakly annotated newborn data during model training. This weakly supervised method achieves state-of-the-art performance for skull-stripping neonatal brain imaging, improving upon existing methods in terms of both the Dice coefficient and Hausdorff distance quantitative metrics. Together, these methods demonstrate the potential of leveraging domain adaptation techniques to bridge the gap between adult and newborn brain MRI data, enabling accurate skull-stripping across diverse populations. The source code and weights of the trained models are publicly available at https://github.com/abbasomidi77/DAUnet.
dc.identifier.citation	Omidi, A. (2025). Unsupervised and weakly supervised domain adaptation of MRI skull-stripping models trained on adult data to newborns (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/120451
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Skull-stripping
dc.subject	Deep Learning
dc.subject	Artificial Intelligence
dc.subject	Machine Learning
dc.subject	Computer Vision
dc.subject	Magnetic Resonance Imaging
dc.subject	Synthetic Data
dc.subject	Infants
dc.subject	Domain Adaptation
dc.subject.classification	Artificial Intelligence
dc.title	Unsupervised and Weakly Supervised Domain Adaptation of MRI Skull-Stripping Models Trained on Adult Data to Newborns
dc.type	master thesis
thesis.degree.discipline	Engineering – Electrical & Computer
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.